Artificial intelligence (AI) has identified two plant-based bioactive compounds with potential as glucagon-like-peptide-1 receptor (GLP-1R) agonists for weight loss as possible alternatives to pharmaceutical weight-loss drugs, but with potentially fewer side effects and oral administration.
Using AI, the work aimed to identify novel, natural-derived bioactive compounds that may activate the GLP-1R, which is the site of action of existing weight loss pharmaceutical drugs including semaglutide (Wegovy, Novo Nordisk) and dual agonist tirzepatide (Zepbound, Eli Lilly).
Presenter Elena Murcia, PhD, of the Structural Bioinformatics and High-Performance Computing Research Group & Eating Disorders Research Unit, Catholic University of Murcia, Murcia, Spain, will be sharing her work at the upcoming European Congress on Obesity (ECO 2024) in May.
Although GLP-1 agonists have shown effectiveness in trials, "there are some side effects associated with their use — gastrointestinal issues such as nausea and vomiting, as well as mental health changes like anxiety and irritability. Recent data has also confirmed that when patients stop treatment, they regain lost weight," she said.
In addition, there is the issue of having to inject the drugs rather than taking them orally due to the peptide nature of existing GLP-1 agonists that risk degradation by stomach enzymes before they exert the required effect.
"Drugs that aren't peptides may have fewer side effects and be easier to administer, meaning they could be given as pills rather than injections," said Murcia.
Other recent research has highlighted two promising non-peptide compounds, TTOAD2 and orforglipron. "These are synthetic, and we were interested in finding natural alternatives," she added.
Natural Versions of Compounds That Activate GLP-1Rs
Drawing on recent understanding around the TTOAD2 and orforglipron compounds, the present work focuses on using AI to identify new non-peptidic, natural-derived bioactive compounds to activate the GLP-1R, according to the researcher in her abstract and a preconference press release from ECO.
Using advanced AI techniques (an in silico approach that entails experimentation by computer), Murcia selected natural molecules as bioactive compounds with GLP-1R agonist activity in a stepwise process that initially used ligand and structure-based virtual screening of over 10,000 compounds, followed by additional visual analysis of the top 100 compounds with the highest similarity to determine their degree of interaction with amino acids on the GLP-1 receptors. Arriving at a shortlist of 65, the researchers synthesized these data to identify the compounds with the highest potential as GLP-1R agonists, and two of these, referred to as Compound A and Compound B — both plant-derived — were found to bind strongly to the key amino acids in a similar way to TTOAD2 and orforglipron.
"These compounds are currently being further investigated for their efficacy in obesity treatment through in vitro analysis," wrote Murcia and her colleagues in their abstract.
Asked to comment on the work, Felix Wong, PhD, postdoctoral fellow at the Broad Institute of MIT and Harvard, Cambridge, Massachusetts, who recently discovered a new class of antibiotics with activity against methicillin-resistant Staphylococcus aureus using deep learning, told Medscape Medical News that, "The promise of AI for drug discovery has increasingly been realized, and just recently we have seen the discoveries of new antibiotics, senolytics, and anti-fibrotic compounds, among others."
"This study, which is based on molecular docking, suggests that similar computational methods can be applied to popular therapeutic areas like GLP-1R agonist discovery," he said, adding that "the study will need experimental validation given that computational predictions can lead to false positives and that natural products are often promiscuous."
Murcia has declared no relevant conflicts. Wong has declared he is cofounder of Integrated Biosciences, an early-stage biotechnology company.
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